选择两个日期之间的DataFrame行

本文介绍了选择两个日期之间的DataFrame行的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在从csv创建一个DataFrame，如下所示:

I am creating a DataFrame from a csv as follows:

stock = pd.read_csv('data_in/' + filename + '.csv', skipinitialspace=True)

DataFrame有一个日期列.有没有一种方法来创建一个新的DataFrame(或仅覆盖现有的DataFrame)，该DataFrame仅包含日期值在指定日期范围内或两个指定日期值之间的行?

The DataFrame has a date column. Is there a way to create a new DataFrame (or just overwrite the existing one) which only contains rows with date values that fall within a specified date range or between two specified date values?

推荐答案

有两种可能的解决方案:

There are two possible solutions:

使用布尔型掩码，然后使用df.loc[mask]
将日期列设置为DatetimeIndex，然后使用df[start_date : end_date]

Use a boolean mask, then use df.loc[mask]
Set the date column as a DatetimeIndex, then use df[start_date : end_date]

使用布尔掩码:

确保df['date']是dtype datetime64[ns]的系列:

Ensure df['date'] is a Series with dtype datetime64[ns]:

df['date'] = pd.to_datetime(df['date'])

制作布尔型蒙版. start_date和end_date可以是datetime.datetime s，np.datetime64 s，pd.Timestamp s甚至日期时间字符串:

Make a boolean mask. start_date and end_date can be datetime.datetimes,np.datetime64s, pd.Timestamps, or even datetime strings:

#greater than the start date and smaller than the end date
mask = (df['date'] > start_date) & (df['date'] <= end_date)

选择子DataFrame:

Select the sub-DataFrame:

df.loc[mask]

或重新分配给df

df = df.loc[mask]

例如，

For example,

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2000-1-1', periods=200, freq='D')
mask = (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10')
print(df.loc[mask])

收益

            0         1         2       date
153  0.208875  0.727656  0.037787 2000-06-02
154  0.750800  0.776498  0.237716 2000-06-03
155  0.812008  0.127338  0.397240 2000-06-04
156  0.639937  0.207359  0.533527 2000-06-05
157  0.416998  0.845658  0.872826 2000-06-06
158  0.440069  0.338690  0.847545 2000-06-07
159  0.202354  0.624833  0.740254 2000-06-08
160  0.465746  0.080888  0.155452 2000-06-09
161  0.858232  0.190321  0.432574 2000-06-10

使用 DatetimeIndex :

Using a DatetimeIndex:

如果您要按日期进行很多选择，则设置快捷方式可能会更快date列首先作为索引.然后您可以使用日期按日期选择行df.loc[start_date:end_date].

If you are going to do a lot of selections by date, it may be quicker to set thedate column as the index first. Then you can select rows by date usingdf.loc[start_date:end_date].

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2000-1-1', periods=200, freq='D')
df = df.set_index(['date'])
print(df.loc['2000-6-1':'2000-6-10'])

收益

                   0         1         2
date
2000-06-01  0.040457  0.326594  0.492136    # <- includes start_date
2000-06-02  0.279323  0.877446  0.464523
2000-06-03  0.328068  0.837669  0.608559
2000-06-04  0.107959  0.678297  0.517435
2000-06-05  0.131555  0.418380  0.025725
2000-06-06  0.999961  0.619517  0.206108
2000-06-07  0.129270  0.024533  0.154769
2000-06-08  0.441010  0.741781  0.470402
2000-06-09  0.682101  0.375660  0.009916
2000-06-10  0.754488  0.352293  0.339337

同时使用Python列表索引，例如seq[start:end]包括start但不包括end，相比之下，熊猫df.loc[start_date : end_date]如果在索引中，则在结果中同时包含和两个端点.但是，start_date和end_date都不必在索引中.

While Python list indexing, e.g. seq[start:end] includes start but not end, in contrast, Pandas df.loc[start_date : end_date] includes both end-points in the result if they are in the index. Neither start_date nor end_date has to be in the index however.

还请注意， pd.read_csv具有一个parse_dates参数，可用于将date列解析为datetime64.因此，如果使用parse_dates，则无需使用df['date'] = pd.to_datetime(df['date']).

Also note that pd.read_csv has a parse_dates parameter which you could use to parse the date column as datetime64s. Thus, if you use parse_dates, you would not need to use df['date'] = pd.to_datetime(df['date']).

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datetime64s